Climate change is one of the trending terms in the world nowadays due to its profound impact on human health and activity. Extreme drought events and desertification are some of the results of climate change. This study utilized the power of AI tools by using the long short-term memory (LSTM) model to predict the drought index for Anbar Province, Iraq. The data from the standardized precipitation evapotranspiration index (SPEI) for 118 years have been used for the current study. The proposed model employed seven different optimizers to enhance the prediction performance. Based on different performance indicators, the results show that the RMSprop and Adamax optimizers achieved the highest accuracy (90.93% and 90.61%, respectively). Additionally, the models forecasted the next 40 years of the SPEI for the study area, where all the models showed an upward trend in the SPEI. In contrast, the best models expected no increase in the severity of drought. This research highlights the vital role of machine learning models and remote sensing in drought forecasting and the significance of these applications by providing accurate climate data for better water resources management, especially in arid regions like that of Anbar province.

The study demonstrates the importance of surface and subsurface hydrology to enhance the performance of the photovoltaic (PV) solar farm in Muthanna Governorate, which lies in the area of ignorance that underestimates the consequences of shallow depth of groundwater (that diminishes efficiency) and the threats of flash floods and dust storms in the desertscape. The study was based on an integrated analytical and quantitative approach, including 2D hydrological modeling (HEC-RAS 2D) and separate statistical modeling relating environmental factors to panel performance, as well as assessing the potential for floating Hydro-PV systems, in an effort to offer a sustainable template to project managers. It was found that groundwater depth is a critical parameter with a negative influence and that a hydrologically designed rainwater harvesting (RWH) system can meet over 60% of the annual washing demand for an anticipated 4.85% improvement in total generation efficiency. Furthermore, the study confirmed that modeling-based engineering protective measures can reduce flooding and sediment erosion in drainage basins by over 70%, and demonstrated that floating hybrid projects (Hydro-PV) are the most strategically sound, yielding the greatest internal rate of return and conserving thousands of cubic meters of water evaporated each year.